Application of support vector machines to diagnosis of late-onset Glutaric Acidemia Type 2 (GA2)

Glutaric Acidemia Type 2 (GA2), or Multiple Acyl-CoA Dehydrogenation Deficiency (MADD), is a genetic metabolic disorder affecting amino acid, fatty acid, and choline mechanisms. While most cases present themselves at birth or at an early age, it is also quite possible to have an onset well into adulthood. For these late-onset patients, the road to diagnosis is often long, painful, and frustrating. In addition, due to late diagnosis they can also suffer from long-lasting effects of their worsening symptoms. The goal of this work is to use support vector machines to detect patterns to aim in the diagnostic process of late-diagnosed GA2 patients.